Fuzzy Preference Programming Method Research Articles

Supplier selection in electronic marketplaces using satisficing and fuzzy AHP

Supplier selection is a critical and demanding task for companies that participate in electronic marketplaces to find suppliers and to execute electronically their transactions. This paper is aimed to suggest a fresh approach for decision support enabling effective supplier selection processes in electronic marketplaces. We introduce an evaluation method with two stages: initial screening of the suppliers through the enforcement of hard constraints on the selection criteria and final supplier evaluation through the application of a modified variant of the Fuzzy Preference Programming (FPP) method. The proposed method alleviates the information overload effect that is inherent in the environment of electronic marketplaces, facilitates an easier elicitation of user preferences through the reduction of necessary user input (i.e. pairwise comparisons) and reduces computational complexity, in terms of the number of linear programs to be solved, in comparison with the original FPP method. The FPP method is adopted and modified accordingly in order to tackle the issue of inconsistency/uncertainty of human preference models. Our approach is demonstrated with the example of a hypothetical metal manufacturing company that finds and selects suppliers in the environment of an electronic marketplace.

Applying consistent fuzzy preference relations to partnership selection

Partnership selection has been important to the formation of a virtual enterprise. Based on the fuzzy preference programming (FPP) method proposed by Mikhailov [Fuzzy analytical approach to partnership selection in formation of virtual enterprises. Omega 2002;30:393–401], this investigation presents a consistent fuzzy preference relations method to select partners. Human thoughts are full of uncertainty, so the decision-makers cannot make exact pairwise comparisons. The FPP method solves this problem using an interval value instead of Saaty's 1–9 scale. In this study, the FPP method is reviewed, and then the consistent fuzzy preference relations method is elucidated. Finally, the presented method is applied to the example addressed by Mikhailov [Fuzzy analytical approach to partnership selection in formation of virtual enterprises. Omega 2002;30:393–401]. This study reveals that the proposed method yields consistent decision rankings from only n - 1 pairwise comparisons—the same number as in Mikhailov's research. Briefly, the presented consistent fuzzy preference relations method is an easy and practical way to provide rankings of partnership in making decision.

Group prioritization in the AHP by fuzzy preference programming method

In this paper we propose a new group fuzzy preference programming (GFPP) method for deriving group priorities from crisp pairwise comparison judgements, provided by multiple decision makers. The assessment of the group priorities is formulated as a fuzzy linear programming problem, maximizing the group's overall satisfaction with the group solution. The GFPP method combines the group synthesis and prioritization stages into a coherent integrated framework, which does not need additional aggregation procedures. The method can easily deal with missing judgements and provides a meaningful indicator for measuring the level of group satisfaction and group consistency. Scope and purpose The increasing complexity of decision-making problems in modern organizations requires the development of innovative decision support tools, which integrate theoretical methods of operations research and recent advancements of computing science. The main objective of this paper is to present a new approach for group decision making with the analytic hierarchy process (AHP), suitable for the development of group decision support systems. We focus on the problem of deriving local group priorities (weights of criteria and scores of alternatives), which is the most important stage of the AHP. The main characteristics of our method for prioritization and its possibilities for group decision-making support are illustrated through detailed examples.

Fuzzy analytic network process and its application to the development of decision support systems

In this paper we propose a fuzzy extension of the analytic network process (ANP) that uses uncertain human preferences as input information in the decision-making process. Instead of the classical Eigenvector prioritization method, employed in the prioritization stage of the ANP, a new fuzzy preference programming method, which obtains crisp priorities from inconsistent interval and fuzzy judgments is applied. The resulting fuzzy ANP enhances the potential of the ANP for dealing with imprecise and uncertain human comparison judgments. It allows for multiple representations of uncertain human preferences, as crisp, interval, and fuzzy judgments and can find a solution from incomplete sets of pairwise comparisons. An important feature of the proposed method is that it measures the inconsistency of the uncertain human preferences by an appropriate consistency index. A prototype decision support system realizing the proposed method is developed, and its performance is illustrated by examples.

Deriving priorities from fuzzy pairwise comparison judgements

A new approach for deriving priorities from fuzzy pairwise comparison judgements is proposed, based on α-cuts decomposition of the fuzzy judgements into a series of interval comparisons. The assessment of the priorities from the pairwise comparison intervals is formulated as an optimisation problem, maximising the decision-maker's satisfaction with a specific crisp priority vector. A fuzzy preference programming method, which transforms the interval prioritisation task into a fuzzy linear programming problem is applied to derive optimal crisp priorities. Aggregating the optimal priorities, which correspond to different α-cut levels enables overall crisp scores of the prioritisation elements to be obtained. A modification of the linear fuzzy preference programming method is also proposed to derive priorities directly from fuzzy judgements, without applying α-cut transformations. The formulation of the prioritisation problem as an optimisation task is similar to the previous approach, but it requires the solution of a non-linear optimisation program. The second approach also derives crisp priorities and has the advantage that it does not need additional aggregation and ranking procedures. Both proposed methods are illustrated by numerical examples and compared to some of the existing fuzzy prioritisation methods.

Fuzzy assessment of priorities with application to competitive bidding

ABSTRACT This paper presents a new fuzzy preference programming method for deriving priorities from inconsistent interval pairwise comparison matrices in the Analytical Hierarchy Process (AHP). The preference programming method of Arbel for elicitation of the priorities from interval matrices transforms the prioritisation problem into a linear program, which is attractive from a computational point of view, however it cannot deal with inconsistent decision—maker judgements. The proposed fuzzy preference programming method represents the inconsistent interval judgements as fuzzy linear constraints and defines a convex non—empty fuzzy feasible area. The assessment of the priorities is stated as an optimisation problem, maximising the decision-maker's satisfaction with the solution of an approximate fuzzy programming problem. The new method is applicable for both crisp and interval inconsistent judgements and can be considered as a generalisation of the classical prioritisation methods in the AHP. An application of this method for the modelling of uncertain judgements and the assessment of priorities in competitive bidding is described.